What you'll do
- You lead and structure SAE Level 4 autonomous driving validation programs across the full development lifecycle, from concept phase to series readiness, homologation and operations.
- You define and operationalize holistic validation strategies for E2E AI‑based AD systems, combining scenario‑based testing, data‑driven validation, simulation, and real‑world testing.
- You translate regulatory, safety and quality requirements (ASPICE, ISO 26262, SOTIF, homologation, ISO PAS 8800) into executable validation concepts, KPIs and release criteria.
- You analyze the validation implications of key AD system components, including camera, radar, lidar, sensor fusion, localization, prediction, planning, control, data pipelines and runtime monitoring.
- You analyze and orchestrate SiL, HiL, MiL and vehicle‑level testing and ensure seamless integration into automated CI/CD pipelines.
- You drive scalable validation approaches for AI models (including coverage metrics, corner‑case detection, data curation strategies, and confidence arguments).
- You define AI model validation KPIs and acceptance thresholds, including scenario coverage, ODD coverage, perception and planning performance, uncertainty calibration, robustness, latency, temporal consistency, rare-event behavior and regression stability.
- You align validation scope and evidence with Type Approval and AD Safety Management Systems (AD‑SMS).
- You act as central interface between AI development teams, system engineers, toolchain providers, test organizations, and external stakeholders (e.g. authorities, partners, suppliers).
- You manage stakeholders at program and management level, including reporting, risk management, decision preparation and escalation.
- You proactively identify validation risks related to AI behavior, operational design domain (ODD) boundaries, and system interactions.
Who you are
- You hold a university degree in Engineering, Computer Science, Artificial Intelligence, or a related field.
- You demonstrate a solid understanding of AI/ML concepts for autonomous driving, including E2E vision-heavy approaches, data‑driven development, and AI‑specific validation challenges.
- You possess a deep understanding of the validation challenges of SAE Level 4 automated driving systems, including ODD definition, scenario coverage, residual risk assessment, safety case development, and evidence-based release decisions.
- You have hands-on experience with simulations, SiL, and HiL testing, ideally integrated into automated CI/CD environments.
- You bring a strong technical understanding of AD system architectures, including modular pipelines, E2E AI models, and hybrid architectures, as well as their impact on validation strategy and safety argumentation.
- You have practical knowledge of camera, radar, and lidar sensor characteristics, sensor fusion principles, calibration, synchronization, degradation effects, and typical failure modes relevant for AD validation.
- You have a proven track record in high-reliability industries (automotive, aerospace, medical), with deep exposure to ASPICE, ISO 26262, SOTIF, and homologation processes.
- You demonstrate strong analytical and structuring skills to translate abstract safety, regulatory, and AI risks into concrete validation strategies.
- You are able to work proactively and independently in agile, cross-functional teams, lead validation initiatives, and align multiple internal and external stakeholders.
Good to know
- You join a global consulting environment with state-of-the-art telecom projects.
- You collaborate with experts in future network technologies and innovative deployment models.
- You grow continuously through training, certifications, and exposure to emerging technolgies.
- You benefit from flexible working models and strong opportunities for career development.